When Deep Learning Meets Electromagnetics

Deep Learning Electromagnetic

Artificial intelligence and deep learning have rapidly become influential technologies in various fields of science. In this article, we will explore the impact of deep learning on electromagnetics science and examine its applications in this field.

Various Types of Regularization


Regularization is a technique used in Machine Learning and Deep Learning models to prevent overfitting. This paper introduces L1, L2, and dropout regularization methods.

Organizations, Members & Access Level Management on the AIEX Platform

Organizations, Members & Access Level Management on the AIEX Platform

Training a highly accurate computer vision model requires many carefully annotated images. Data gathering and annotation take time and might often prove to be hard. As the saying goes, nothing is particularly hard if you divide it into small jobs (and assign it to a team of people). This chapter details the teamwork tools implemented on the AIEX deep learning platform. An overview of organizations, projects, workgroups, and some of the best practices will be outlined in this paper.

Trauma Detection on Pelvic Radiographs using Computer Vision Algorithms

Trauma Detection on Pelvic Radiographs using Computer Vision Algorithms

A timely and accurate diagnosis of the proximal femur and pelvis injuries in trauma patients is essential to saving their lives. High-quality clinical trauma care and treatment require both physician experience and radiography images. A multiscale deep learning algorithm called PelviXNet has been developed to rapidly and accurately detect most kinds of pelvic and hip fractures.

How Backbone Works


Backbone is a network that extracts a feature map of the input image , the map is then utilized by the rest of the network. The purpose of this paper is to introduce the concept of backbone and how it fits in the AIEX platform.

The printing industry is joining the 4.0th industrial revolution. Spring is coming!

printing industry

Deep Learning algorithms were used to automate the quality control of printing processes. The developed model can automatically classify and detect some printing defects with an accuracy rate of 98.4%. This approach makes it easier to persuade business leaders to think of deep architecture as a possible solution for their challenges.

Logistics monitoring is a piece of cake by computer vision

Logistics monitoring

Computer vision (CV) in logistics can promote the ability of manufacturers and managers to detect problems and optimize processes. The company owners can take advantage of artificial intelligence (AI) in logistics to enhance efficiency and effectiveness by accelerating manual processes as well as improving safety while significantly reducing operational costs.